Rapid classification of soils from different mining areas by laser-induced breakdown spectroscopy (LIBS) coupled with a PCA-based convolutional neural network-Reference-Cited by-同舟云学术

Rapid classification of soils from different mining areas by laser-induced breakdown spectroscopy (LIBS) coupled with a PCA-based convolutional neural network

Published:2021 Issue:11 Volume:36 Page:2509-2518
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Hao Wenyuan¹^ORCID,Hao Xiaojian¹,Yang Yanwei²,Liu Xuanda¹^ORCID,Liu Yekun¹,Sun Peng¹,Sun Ran¹

Affiliation:

1. Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, People's Republic of China

2. Luliang University, Department of Physics, Lvliang, Shanxi, 033000, China

Abstract

The results of this article show that 2D-CNN has great potential in the field of soil recognition and classification combine with LIBS, and provides a new and reliable data processing method for LIBS to classify materials with similar chemical properties.

Funder

National Natural Science Foundation of China

Fund for Shanxi Key Subjects Construction

Publisher

Royal Society of Chemistry (RSC)

Subject

Spectroscopy,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/JA/D1JA00078K

Reference41 articles.

1. A review of soil heavy metal pollution from mines in China: Pollution and health risk assessment

2. Qualitative soil mineral analysis by EDXRF, XRD and AAS probes

3. Trace element concentrations in soils along urbanization gradients in the city of Wien, Austria

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